
## Importing Required Libraries

import time
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.figure_factory as ff
%matplotlib inline


## Creating DataFrames by reading MS Excel Files

df1 = pd.read_excel("39c00379-17c1-4120-96b7-fa372e26f8f0.xlsx")
df2 = pd.read_excel("6481b945-7549-44e5-a65b-8b8eb8b92c87.xlsx")


## File1 Information

print(df1.shape, "\n")
print(df1.columns, "\n")
df1.info()

(222697, 8) 

Index(['Inspection ID', 'FEI Number', 'Legal Name', 'Inspection End Date',
       'Program Area', 'Act/CFR Number', 'Short Description',
       'Long Description'],
      dtype='object') 

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 222697 entries, 0 to 222696
Data columns (total 8 columns):
 #   Column               Non-Null Count   Dtype         
---  ------               --------------   -----         
 0   Inspection ID        222697 non-null  int64         
 1   FEI Number           222697 non-null  int64         
 2   Legal Name           222697 non-null  object        
 3   Inspection End Date  222697 non-null  datetime64[ns]
 4   Program Area         222697 non-null  object        
 5   Act/CFR Number       222516 non-null  object        
 6   Short Description    222697 non-null  object        
 7   Long Description     222694 non-null  object        
dtypes: datetime64[ns](1), int64(2), object(5)
memory usage: 13.6+ MB


## File1 DataFrame

df1.head(15)


## File2 Information

print(df2.shape, "\n")
print(df2.columns, "\n")
df2.info()

(257624, 14) 

Index(['FEI Number', 'Legal Name', 'City', 'State', 'Zip', 'Country/Area',
       'Fiscal Year', 'Inspection ID', 'Posted Citations',
       'Inspection End Date', 'Classification', 'Project Area', 'Product Type',
       'FMD-145 Date'],
      dtype='object') 

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 257624 entries, 0 to 257623
Data columns (total 14 columns):
 #   Column               Non-Null Count   Dtype         
---  ------               --------------   -----         
 0   FEI Number           257624 non-null  int64         
 1   Legal Name           257624 non-null  object        
 2   City                 257624 non-null  object        
 3   State                257624 non-null  object        
 4   Zip                  257624 non-null  object        
 5   Country/Area         257624 non-null  object        
 6   Fiscal Year          257624 non-null  int64         
 7   Inspection ID        257624 non-null  int64         
 8   Posted Citations     257624 non-null  object        
 9   Inspection End Date  257624 non-null  datetime64[ns]
 10  Classification       257624 non-null  object        
 11  Project Area         257624 non-null  object        
 12  Product Type         257624 non-null  object        
 13  FMD-145 Date         257624 non-null  object        
dtypes: datetime64[ns](1), int64(3), object(10)
memory usage: 27.5+ MB


## File2 DataFrame

df2.head(15)


## Removing Space from Columns Names

df1.columns = df1.columns.str.replace(' ', '')
df2.columns = df2.columns.str.replace(' ', '')


## Merging both DataFrames based on their common columns (FEI Number, Inspection ID, Inspection End Date, Legal Name)

merged_df = pd.merge(df1, df2, how="left", on=["FEINumber", "InspectionID", "InspectionEndDate", "LegalName"])
merged_df.columns = merged_df.columns.str.replace(' ', '')
merged_df


## Printing Number of values in each dataframes

print(df1.isnull().sum(),"\n")
print(df2.isnull().sum(),"\n")
print(merged_df.isnull().sum())

InspectionID           0
FEINumber              0
LegalName              0
InspectionEndDate      0
ProgramArea            0
Act/CFRNumber        181
ShortDescription       0
LongDescription        3
dtype: int64 

FEINumber            0
LegalName            0
City                 0
State                0
Zip                  0
Country/Area         0
FiscalYear           0
InspectionID         0
PostedCitations      0
InspectionEndDate    0
Classification       0
ProjectArea          0
ProductType          0
FMD-145Date          0
dtype: int64 

InspectionID           0
FEINumber              0
LegalName              0
InspectionEndDate      0
ProgramArea            0
Act/CFRNumber        199
ShortDescription       0
LongDescription        3
City                   0
State                  0
Zip                    0
Country/Area           0
FiscalYear             0
PostedCitations        0
Classification         0
ProjectArea            0
ProductType            0
FMD-145Date            0
dtype: int64


## Value counts of Legal Name 

pd.DataFrame(merged_df['LegalName'].value_counts())


## Value counts of Program Area 

pd.DataFrame(merged_df['ProgramArea'].value_counts())


## Value counts of Cities 

pd.DataFrame(merged_df['City'].value_counts())


## Value counts of State  

pd.DataFrame(merged_df['State'].value_counts())


## Value counts of Country/Area

pd.DataFrame(merged_df['Country/Area'].value_counts())


## Value counts of Classifications 

pd.DataFrame(merged_df['Classification'].value_counts())


## Value counts of Project Areas

pd.DataFrame(merged_df['ProjectArea'].value_counts())


## Value counts of Product Type 

pd.DataFrame(merged_df['ProductType'].value_counts())


## Reference - https://stackoverflow.com/questions/38783027/jupyter-notebook-display-two-pandas-tables-side-by-side

from IPython.display import display_html
from itertools import chain,cycle
def display_side_by_side(*args,titles=cycle([''])):
    html_str=''
    for df,title in zip(args, chain(titles,cycle(['</br>'])) ):
        html_str+='<th style="text-align:center"><td style="vertical-align:top">'
        html_str+=f'<h2>{title}</h2>'
        html_str+=df.to_html().replace('table','table style="display:inline"')
        html_str+='</td></th>'
    display_html(html_str,raw=True)


## Value counts of Short Description & Long Description 

short_df = pd.DataFrame(merged_df['ShortDescription'].value_counts().head(10))
long_df = pd.DataFrame(merged_df['LongDescription'].value_counts().head(10))
display_side_by_side(short_df,long_df, titles=['Short Description','Long Description'])


## Product Type wise Short Description - Maryland State

byProductType = pd.DataFrame(merged_df.groupby('State').ShortDescription.value_counts())
pd.DataFrame(byProductType.unstack().T['Maryland'].sort_values(ascending=False).head(15))


## Product Type wise Long Description - Maryland State

byProductType = pd.DataFrame(merged_df.groupby('State').LongDescription.value_counts())
pd.DataFrame(byProductType.unstack().T['Maryland'].sort_values(ascending=False).head(15))


## Product Type wise Short Description - Biologics

byProductType = pd.DataFrame(merged_df.groupby('ProductType').ShortDescription.value_counts())
byProductType.unstack().T.sort_values(by=['Biologics'], ascending=False).head(15)


## Product Type wise Long Description - Biologics

byClassification = pd.DataFrame(merged_df.groupby('ProductType').LongDescription.value_counts())
byClassification.unstack().T.sort_values(by=['Biologics'], ascending=False).head(15)


## Product Type wise Short Description - Devices

byClassification = pd.DataFrame(merged_df.groupby('ProductType').ShortDescription.value_counts())
byClassification.unstack().T.sort_values(by=['Devices'], ascending=False).head(15)


## Product Type wise Long Description - Devices

byClassification = pd.DataFrame(merged_df.groupby('ProductType').LongDescription.value_counts())
byClassification.unstack().T.sort_values(by=['Devices'], ascending=False).head(15)


## Product Type wise Short Description - Drugs

byClassification = pd.DataFrame(merged_df.groupby('ProductType').ShortDescription.value_counts())
byClassification.unstack().T.sort_values(by=['Drugs'], ascending=False).head(15)


## Product Type wise Long Description - Drugs

byClassification = pd.DataFrame(merged_df.groupby('ProductType').LongDescription.value_counts())
byClassification.unstack().T.sort_values(by=['Drugs'], ascending=False).head(15)


## Product Type wise Short Description - Veterinary

byClassification = pd.DataFrame(merged_df.groupby('ProductType').ShortDescription.value_counts())
byClassification.unstack().T.sort_values(by=['Veterinary'], ascending=False).head(15)


## Product Type wise Long Description - Veterinary

byClassification = pd.DataFrame(merged_df.groupby('ProductType').LongDescription.value_counts())
byClassification.unstack().T.sort_values(by=['Veterinary'], ascending=False).head(15)


## Product Type wise Short Description - Food/Cosmetics

byClassification = pd.DataFrame(merged_df.groupby('ProductType').ShortDescription.value_counts())
byClassification.unstack().T.sort_values(by=['Food/Cosmetics'], ascending=False).head(15)


## Bar-graph Showing biggest problems found during Food/Cosmetic Inspection

byClassification.unstack().T['Food/Cosmetics'].sort_values(ascending=False).head(15).plot(kind='bar')

<AxesSubplot:xlabel='None,ShortDescription'>


## Product Type wise Long Description - Food/Cosmetics

byClassification = pd.DataFrame(merged_df.groupby('ProductType').LongDescription.value_counts())
byClassification.unstack().T.sort_values(by=['Food/Cosmetics'], ascending=False).head(15)


## Product Type wise Short Description - Tobacco

byClassification = pd.DataFrame(merged_df.groupby('ProductType').ShortDescription.value_counts())
byClassification.unstack().T.sort_values(by=['Tobacco'], ascending=False).head(15)


## Product Type wise Long Description - Tobacco

byClassification = pd.DataFrame(merged_df.groupby('ProductType').LongDescription.value_counts())
byClassification.unstack().T.sort_values(by=['Tobacco'], ascending=False).head(15)


country_df = pd.DataFrame(merged_df[['Country/Area', 'FiscalYear']])

for i in country_df.index:
    if country_df['Country/Area'][i] != "United States":
        country_df['Country/Area'][i]="Foreign"


## Pie-Chart showing Domestic vs Foreign Inspection Count

plt.figure(figsize=(8,8))
country_df['Country/Area'].value_counts().plot(kind='pie', autopct='%.1f%%')
plt.title("Domestic/Foreign")
plt.show()


## Data pre-processing before generating visuals.

state_codes = {
    'District of Columbia' : 'dc','Mississippi': 'MS', 'Oklahoma': 'OK', 
    'Delaware': 'DE', 'Minnesota': 'MN', 'Illinois': 'IL', 'Arkansas': 'AR', 
    'New Mexico': 'NM', 'Indiana': 'IN', 'Maryland': 'MD', 'Louisiana': 'LA', 
    'Idaho': 'ID', 'Wyoming': 'WY', 'Tennessee': 'TN', 'Arizona': 'AZ', 
    'Iowa': 'IA', 'Michigan': 'MI', 'Kansas': 'KS', 'Utah': 'UT', 
    'Virginia': 'VA', 'Oregon': 'OR', 'Connecticut': 'CT', 'Montana': 'MT', 
    'California': 'CA', 'Massachusetts': 'MA', 'West Virginia': 'WV', 
    'South Carolina': 'SC', 'New Hampshire': 'NH', 'Wisconsin': 'WI',
    'Vermont': 'VT', 'Georgia': 'GA', 'North Dakota': 'ND', 
    'Pennsylvania': 'PA', 'Florida': 'FL', 'Alaska': 'AK', 'Kentucky': 'KY', 
    'Hawaii': 'HI', 'Nebraska': 'NE', 'Missouri': 'MO', 'Ohio': 'OH', 
    'Alabama': 'AL', 'Rhode Island': 'RI', 'South Dakota': 'SD', 
    'Colorado': 'CO', 'New Jersey': 'NJ', 'Washington': 'WA', 
    'North Carolina': 'NC', 'New York': 'NY', 'Texas': 'TX', 
    'Nevada': 'NV', 'Maine': 'ME', 'Puerto Rico': 'PR', 'Virgin Islands': 'VI',
    'Northern Mariana Islands':'NI','Guam':'GM','American Samoa':'AS'
}

us_df = merged_df[merged_df['Country/Area'] == 'United States']
us_df['State Code'] = us_df['State'].apply(lambda x : state_codes[x])
us_df.drop_duplicates(subset=['InspectionID'])


## Map showing State-wise Inspection Count
fig = px.choropleth(us_df, locations='State Code', color=us_df['InspectionID'], color_continuous_scale='spectral_r', hover_name='State', locationmode='USA-states', labels={'InspectionID':'Inspection count'}, scope='usa')
fig.show()


## Number of Inspections Yearly

pd.DataFrame(merged_df['FiscalYear'].value_counts())


## Country-wise and Year-wise count of Inspections

byFiscalYear = pd.DataFrame(country_df.groupby('Country/Area').FiscalYear.value_counts())
byFiscalYear.unstack().T


# changing the rc parameters and plotting a line plot
plt.rcParams['figure.figsize'] = [15, 5]

# plt.figure(figsize=(50,50))
byFiscalYear.unstack().T.plot(kind='line')
plt.show()


## State-wise and Year-wise count of Inspections

byFiscalYear = pd.DataFrame(merged_df.groupby('FiscalYear').State.value_counts())
byFiscalYear.unstack().T


# changing the rc parameters and plotting a line plot
plt.rcParams['figure.figsize'] = [30, 30]

# plt.figure(figsize=(50,50))
byFiscalYear.unstack().plot(kind='line')
plt.show()
print("Double Click on Graph to view it properly.")

Double Click on Graph to view it properly.


## Classification-wise product type

byClassification = pd.DataFrame(merged_df.groupby('Classification').ProductType.value_counts())
byClassification.unstack().T


# changing the rc parameters and plotting a line plot
plt.rcParams['figure.figsize'] = [8, 8]

#plt.figure(figsize=(25,12))
byClassification.unstack().T.plot(kind='bar', stacked=True)

<AxesSubplot:xlabel='None,ProductType'>


## Classification-wise Project Area

byClassification = pd.DataFrame(merged_df.groupby('Classification').ProjectArea.value_counts())
byClassification.unstack().T


# changing the rc parameters and plotting a line plot
plt.rcParams['figure.figsize'] = [300, 5]

#plt.figure(figsize=(25,12))
byClassification.unstack().plot(kind='pie', subplots=True)
print("Double click on the graphs to see them properly.")

Double click on the graphs to see them properly.


## State-wise and Year-wise count of Inspections

byClassification = pd.DataFrame(merged_df.groupby('Classification').State.value_counts())
byClassification.unstack().T


# changing the rc parameters and plotting a line plot
plt.rcParams['figure.figsize'] = [600, 7]

#plt.figure(figsize=(25,12))
byClassification.unstack().plot(kind='pie', subplots=True)
print("Double click on the graphs to see them properly.")

Double click on the graphs to see them properly.

	Inspection ID	FEI Number	Legal Name	Inspection End Date	Program Area	Act/CFR Number	Short Description	Long Description
0	1167299	3017202975	PAVmed Inc.	2022-04-15	Devices	21 CFR 820.100(b)	Documentation	Corrective and preventive action activities an...
1	1167299	3017202975	PAVmed Inc.	2022-04-15	Devices	21 CFR 820.198(a)	Lack of or inadequate complaint procedures	Procedures for receiving, reviewing, and evalu...
2	1167002	3006463947	Ximedica LLC	2022-04-13	Devices	21 CFR 820.30(g)	Design validation - production units	The design was not validated under defined ope...
3	1167016	3009492128	Exogenesis Corporation	2022-04-12	Devices	21 CFR 820.70(c)	Environmental control Lack of or inadequate p...	Procedures to control environmental conditions...
4	1167016	3009492128	Exogenesis Corporation	2022-04-12	Devices	21 CFR 820.250(b)	Sampling plans	Sampling plans are not based on valid statisti...
5	1167392	3004217244	Trio Community Meals	2022-04-11	Foods	21 CFR 123.6(b)	No HACCP plan	You do not have a written HACCP plan that outl...
6	1167392	3004217244	Trio Community Meals	2022-04-11	Foods	21 CFR 123.9(b)(1)	Record retention	Your monitoring records are not maintained at ...
7	1167392	3004217244	Trio Community Meals	2022-04-11	Foods	21 CFR 123.11(c)	Sanitation Records	You are not maintaining sanitation control rec...
8	1166559	3006468808	Metro-Pack, Inc.	2022-04-07	Foods	21 CFR 111.15(d)(2)	Pest control measures	You did not take effective measures to exclude...
9	1166559	3006468808	Metro-Pack, Inc.	2022-04-07	Foods	21 CFR 111.475(b)(1)	Written procedures - holding; distributing	You did not make and keep written procedures f...
10	1166739	3007335286	Kiryas Yoel World Famous Kosher Bakery, Inc.	2022-04-07	Foods	21 CFR 117.20(b)	Plant Construction and Design	Your plant was not designed to facilitate main...
11	1166739	3007335286	Kiryas Yoel World Famous Kosher Bakery, Inc.	2022-04-07	Foods	21 CFR 117.35(d)	Sanitation of food contact surfaces - frequency	You did not clean and sanitize your utensils o...
12	1166739	3007335286	Kiryas Yoel World Famous Kosher Bakery, Inc.	2022-04-07	Foods	21 CFR 117.40	Equipment and Utensils - Design and Maintenance	Your equipment and utensils were not designed ...
13	1166435	3010610364	Flushing Meat Provision Corp.	2022-04-06	Foods	21 CFR 117.35(c)	Pest Control	You did not exclude pests from your food plant...
14	1166444	3009519707	Revolutionary Science	2022-04-06	Devices	21 CFR 803.17(a)(1)	Lack of System for Event Evaluations	The written MDR Procedure does not include an ...

	FEI Number	Legal Name	City	State	Zip	Country/Area	Fiscal Year	Inspection ID	Posted Citations	Inspection End Date	Classification	Project Area	Product Type	FMD-145 Date
0	3017202975	PAVmed Inc.	Foxboro	Massachusetts	02035	United States	2022	1167299	Yes	2022-04-15	Voluntary Action Indicated (VAI)	Compliance: Devices	Devices	-
1	3017202975	PAVmed Inc.	Foxboro	Massachusetts	02035	United States	2022	1167299	Yes	2022-04-15	No Action Indicated (NAI)	Postmarket Assurance: Devices	Devices	-
2	3000138835	Ten Ren Tea and Ginseng Co Inc	New York	New York	10013	United States	2022	1167517	No	2022-04-15	No Action Indicated (NAI)	Foodborne Biological Hazards	Food/Cosmetics	-
3	3007086121	Mass Commodities, LLC	Miami	Florida	33131	United States	2022	1166850	No	2022-04-14	No Action Indicated (NAI)	Foodborne Biological Hazards	Food/Cosmetics	-
4	3020871464	Blood Assurance inc	Hermitage	Tennessee	37076	United States	2022	1167480	No	2022-04-14	No Action Indicated (NAI)	Blood and Blood Products	Biologics	-
5	3020871464	Blood Assurance inc	Hermitage	Tennessee	37076	United States	2022	1167480	No	2022-04-14	No Action Indicated (NAI)	Vaccines and Allergenic Products	Biologics	-
6	3001877793	Marriott P.R. Management Corporation	San Juan	Puerto Rico	00907	United States	2022	1166891	No	2022-04-13	No Action Indicated (NAI)	Foodborne Biological Hazards	Food/Cosmetics	-
7	3006463947	Ximedica LLC	Providence	Rhode Island	02907	United States	2022	1167002	Yes	2022-04-13	Voluntary Action Indicated (VAI)	Compliance: Devices	Devices	-
8	3006463947	Ximedica LLC	Providence	Rhode Island	02907	United States	2022	1167002	Yes	2022-04-13	No Action Indicated (NAI)	Postmarket Assurance: Devices	Devices	-
9	3006948344	Snavely's Mill, Inc.	Palmyra	Pennsylvania	17078	United States	2022	1167076	No	2022-04-13	No Action Indicated (NAI)	Foodborne Biological Hazards	Food/Cosmetics	-
10	3006948344	Snavely's Mill, Inc.	Palmyra	Pennsylvania	17078	United States	2022	1167076	No	2022-04-13	No Action Indicated (NAI)	Monitoring of Marketed Animal Drugs, Feed, and...	Veterinary	-
11	3004354270	Ralphs/Food4Less Returns Processing Center	Ontario	California	91761	United States	2022	1167100	No	2022-04-13	No Action Indicated (NAI)	Monitoring of Marketed Animal Drugs, Feed, and...	Veterinary	-
12	3014553208	DELAVIUDA USA INC	Coral Gables	Florida	33134	United States	2022	1166400	No	2022-04-12	No Action Indicated (NAI)	Foodborne Biological Hazards	Food/Cosmetics	-
13	3012386632	OKE USA FRUIT COMPANY	West Bridgewater	Massachusetts	02379	United States	2022	1166922	No	2022-04-12	No Action Indicated (NAI)	Foodborne Biological Hazards	Food/Cosmetics	-
14	3009492128	Exogenesis Corporation	Billerica	Massachusetts	01821	United States	2022	1167016	Yes	2022-04-12	Voluntary Action Indicated (VAI)	Compliance: Devices	Devices	-

	InspectionID	FEINumber	LegalName	InspectionEndDate	ProgramArea	Act/CFRNumber	ShortDescription	LongDescription	City	State	Zip	Country/Area	FiscalYear	PostedCitations	Classification	ProjectArea	ProductType	FMD-145Date
0	1167299	3017202975	PAVmed Inc.	2022-04-15	Devices	21 CFR 820.100(b)	Documentation	Corrective and preventive action activities an...	Foxboro	Massachusetts	02035	United States	2022	Yes	Voluntary Action Indicated (VAI)	Compliance: Devices	Devices	-
1	1167299	3017202975	PAVmed Inc.	2022-04-15	Devices	21 CFR 820.100(b)	Documentation	Corrective and preventive action activities an...	Foxboro	Massachusetts	02035	United States	2022	Yes	No Action Indicated (NAI)	Postmarket Assurance: Devices	Devices	-
2	1167299	3017202975	PAVmed Inc.	2022-04-15	Devices	21 CFR 820.198(a)	Lack of or inadequate complaint procedures	Procedures for receiving, reviewing, and evalu...	Foxboro	Massachusetts	02035	United States	2022	Yes	Voluntary Action Indicated (VAI)	Compliance: Devices	Devices	-
3	1167299	3017202975	PAVmed Inc.	2022-04-15	Devices	21 CFR 820.198(a)	Lack of or inadequate complaint procedures	Procedures for receiving, reviewing, and evalu...	Foxboro	Massachusetts	02035	United States	2022	Yes	No Action Indicated (NAI)	Postmarket Assurance: Devices	Devices	-
4	1167002	3006463947	Ximedica LLC	2022-04-13	Devices	21 CFR 820.30(g)	Design validation - production units	The design was not validated under defined ope...	Providence	Rhode Island	02907	United States	2022	Yes	Voluntary Action Indicated (VAI)	Compliance: Devices	Devices	-
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
313042	540313	3000203414	Avanti Foods, Inc.	2008-10-01	Foods	21 CFR 123.6(c)(4)	Monitoring - adequacy	Your HACCP plan lists monitoring procedures an...	Milwaukee	Wisconsin	53214	United States	2009	Yes	Voluntary Action Indicated (VAI)	Foodborne Biological Hazards	Food/Cosmetics	-
313043	540313	3000203414	Avanti Foods, Inc.	2008-10-01	Foods	21 CFR 123.7(d)	Corrective action documentation	You do not have records that document correcti...	Milwaukee	Wisconsin	53214	United States	2009	Yes	Voluntary Action Indicated (VAI)	Foodborne Biological Hazards	Food/Cosmetics	-
313044	541050	3003895195	Little Lad's Bakery, Inc.	2008-10-01	Foods	21 CFR 110.35(a)	Buildings/good repair	Failure to maintain physical facilities in rep...	Corinth	Maine	04427	United States	2009	Yes	Voluntary Action Indicated (VAI)	Foodborne Biological Hazards	Food/Cosmetics	-
313045	541050	3003895195	Little Lad's Bakery, Inc.	2008-10-01	Foods	21 CFR 110.35(a)	Buildings/sanitary	Failure to maintain buildings, fixtures, or ot...	Corinth	Maine	04427	United States	2009	Yes	Voluntary Action Indicated (VAI)	Foodborne Biological Hazards	Food/Cosmetics	-
313046	541050	3003895195	Little Lad's Bakery, Inc.	2008-10-01	Foods	21 CFR 110.35(c)	Lack of effective pest exclusion	Effective measures are not being taken to excl...	Corinth	Maine	04427	United States	2009	Yes	Voluntary Action Indicated (VAI)	Foodborne Biological Hazards	Food/Cosmetics	-

	LegalName
Titan Medical Enterprises, Inc. DBA U.S. Apothecary Labs	385
Tropical Bakery Wholesale Corp	215
Invacare Corporation	210
Agropharma Laboratories Inc.	202
H & P Industries, Inc.	198
...	...
Phu Thinh Food Processing Export Joint Stock Company	1
Pigeon Feeds Inc.	1
Lenter, Fred	1
Dustin A. Bubar	1
Shantou Chaonan Liangying Hegao Plastic Toy Factory	1

	ProgramArea
Foods	168969
Devices	72912
Drugs	42906
Veterinary Medicine	8522
Bioresearch Monitoring	6613
Human Tissue for Transplantation	4643
Biologics	4606
Parts 1240 and 1250	2707
Radiologic Health	940
Part 11 Compliance	229

Exploratory Data Analysis - US Food & Drug Administration¶

Data Source 1 –¶

Data Source 2 –¶

Breakdown of the project –¶

3Ps Problem faced by FDA - Procedure, Pest & Protocol¶

Short Description

Long Description

Pest Control is One of the Biggest Problem in Maryland¶

Procedures are not being followed in Biologics, Devices, Drugs, and Veterinary¶

Yes, Looking at the volume of Short & Long Decriptions on Procedures not being followed properly shows our thesis is correct.¶

Sanitation & Bugs are the biggest problem in Food Industry¶

Yes, Looking at the volume of Short & Long Decriptions on Procedures not being followed properly shows our thesis is correct.¶

Storage is one of main issues of Tobacco Industry¶

Where does Most Inspections Happens Domestic/Foreign¶

Which state has more Inspections till now?¶

Has Inspections decresed overtime?¶

Yes, Number of inspections has decresed overtime¶

Does Classification has any relation with Product Type or Project Area¶

Yes, Classifications are changing according to Product Type and Project area¶

Does Classification difffers state-wise¶

Yes, Classification differes state-wise.¶

	City
Miami	4668
Dallas	2748
Brooklyn	2556
Chicago	2529
Hialeah	2343
...	...
Kodama-Gun	1
A.Makham	1
Moorabbin	1
Arlington Hts	1
Spring Church	1

	State
-	41176
California	39319
Florida	31894
New York	21121
Texas	15142
Ohio	10438
Illinois	9634
Pennsylvania	8718
New Jersey	8654
Massachusetts	8355
Washington	8120
Michigan	7247
Wisconsin	6857
Colorado	6771
North Carolina	5909
Puerto Rico	5770
Georgia	5618
Minnesota	5119
Utah	4894
Tennessee	4353
Indiana	4171
Missouri	4100
Virginia	3980
Maryland	3954
Kentucky	3821
Oregon	3673
Connecticut	3080
Arizona	2815
Maine	2303
South Carolina	1943
Kansas	1860
Louisiana	1775
Iowa	1609
Oklahoma	1567
Idaho	1454
New Mexico	1449
Rhode Island	1446
Alabama	1419
Nevada	1405
Hawaii	1276
New Hampshire	1137
Arkansas	1129
Vermont	1113
Mississippi	792
Nebraska	771
West Virginia	685
Montana	659
Alaska	645
District of Columbia	418
Delaware	386
North Dakota	296
Wyoming	258
South Dakota	227
Virgin Islands	219
Guam	60
American Samoa	43

	Country/Area
United States	271871
India	2803
Canada	2767
Germany	2353
China	2236
...	...
Bosnia-Hercegovina	2
Egypt	2
Cape Verde	2
Faroe Islands	2
Tunisia	2

	Classification
Voluntary Action Indicated (VAI)	215608
Official Action Indicated (OAI)	54377
No Action Indicated (NAI)	43062

	ProjectArea
Foodborne Biological Hazards	111517
Compliance: Devices	41372
Food Composition, Standards, Labeling and Econ	35218
Drug Quality Assurance	30502
Postmarket Assurance: Devices	28573
Monitoring of Marketed Animal Drugs, Feed, and Devices	15017
Bioresearch Monitoring	9921
Pesticides and Chemical Contaminants	9420
Food and Color Additives Petition Review	8377
Blood and Blood Products	5653
Human Cellular, Tissue, and Gene Therapies	4609
Technical Assistance: Food and Cosmetics	4468
Colors and Cosmetics Technology	3020
Unapproved and Misbranded Drugs	2658
Radiation Control and Health Safety Act	1758
Pre-Approval Eval. of Animal Drugs and Food Additives	378
Vaccines and Allergenic Products	299
Postmarket Surv. and Epidemiology	161
Over-the-Counter Drug Evaluation	87
Molecular Biology and Natural Toxins	28
Prescription Drug Advertising and Labelling	8
Tobacco Post-Market Activities	3

	ProductType
Food/Cosmetics	172054
Devices	75094
Drugs	39138
Veterinary	15427
Biologics	11331
Tobacco	3

	ShortDescription
Lack of or inadequate procedures	6099
Sanitation monitoring	5124
Lack of effective pest exclusion	4967
Lack of or inadequate complaint procedures	4341
Screening	3293
Documentation	3287
HACCP plan implementation	3037
Floors, walls and ceilings	2742
Lack of Written MDR Procedures	2622
Buildings/sanitary	2537

	LongDescription
Procedures for corrective and preventive action have not been adequately established.	4537
Procedures for receiving, reviewing, and evaluating complaints by a formally designated unit have not been adequately established.	3311
Failure to provide adequate screening or other protection against pests.	3293
Failure to maintain buildings, fixtures, or other physical facilities in a sanitary condition.	2537
You did not develop an FSVP.	2237
Effective measures are not being taken to exclude pests from the processing areas.	2220
Your HACCP plan does not list the food safety hazards that are reasonably likely to occur.	2214
Your HACCP plan does not list one or more critical control points that are necessary for each of the identified food safety hazards.	2105
You do not have a written HACCP plan that outlines controls for a food safety hazard that is reasonably likely to occur.	1866
All reasonable precautions are not taken to ensure that production procedures do not contribute contamination from any source.	1839

	ProductType	Biologics	Devices	Drugs	Food/Cosmetics	Tobacco	Veterinary
	ShortDescription
ShortDescription	Establish, maintain and follow manufacturing SOPs	985.0	1.0	1.0	NaN	NaN	NaN
	Risk factors, clinical evidence	365.0	1.0	1.0	NaN	NaN	NaN
	Thorough investigations	327.0	2.0	4.0	NaN	NaN	NaN
	Testing, screening, donor eligibility procedures	273.0	2.0	NaN	NaN	NaN	NaN
	Concurrent documentation	232.0	3.0	2.0	NaN	NaN	NaN
	Procedures for all other requirements	220.0	1.0	NaN	NaN	NaN	NaN
	Required records	213.0	1.0	1.0	NaN	NaN	NaN
	Person performing, test results, interpretation	208.0	1.0	NaN	NaN	NaN	NaN
	FD-1572, protocol compliance	204.0	9.0	1339.0	1.0	NaN	3.0
	Biological product deviation report	203.0	4.0	5.0	NaN	NaN	NaN
	Procedures to meet core CTGP	186.0	8.0	NaN	NaN	NaN	NaN
	Determination based on screening and testing	181.0	2.0	NaN	NaN	NaN	NaN
	Case history records- inadequate or inadequate	140.0	5.0	796.0	NaN	NaN	3.0
	Responsible person to determine, document	132.0	NaN	NaN	NaN	NaN	NaN
	Accurate, indelible, legible	131.0	NaN	NaN	NaN	NaN	NaN

	ProductType	Biologics	Devices	Drugs	Food/Cosmetics	Tobacco	Veterinary
	LongDescription
LongDescription	Donors were not screened by a review of relevant medical records for risk factors of communicable disease agents and diseases.	197.0	NaN	1.0	NaN	NaN	NaN
	HCT/P donors were not determined to be eligible based on the results of donor screening and testing.	181.0	2.0	NaN	NaN	NaN	NaN
	Failure to submit a biological product deviation report within 45 days from the date you acquired information suggesting that a reportable event occurred.	143.0	3.0	5.0	NaN	NaN	NaN
	Donors were not screened by a review of relevant medical records for risk factors and clinical evidence of communicable disease agents and diseases.	136.0	1.0	NaN	NaN	NaN	NaN
	Failure to perform a thorough investigation and make a record of the conclusions and follow-up of an unexplained discrepancy.	129.0	1.0	NaN	NaN	NaN	NaN
	Failure to use supplies and reagents in a manner consistent with instructions provided by the manufacturer.	110.0	NaN	NaN	NaN	NaN	NaN
	An investigation was not conducted in accordance with the investigational plan.	104.0	101.0	569.0	NaN	NaN	2.0
	Donor specimens used for testing of communicable disease agents were not collected at the appropriate time.	95.0	NaN	NaN	NaN	NaN	NaN
	An investigation was not conducted in accordance with the signed statement of investigator and investigational plan.	88.0	3.0	645.0	1.0	NaN	NaN
	Storage temperatures of HCT/Ps were not recorded and maintained.	86.0	2.0	NaN	NaN	NaN	NaN
	The eligibility of an HCT/P donor was not determined and documented by a responsible person, based on results of donor screening and donor testing.	84.0	NaN	NaN	NaN	NaN	NaN
	All records pertinent to a lot or unit were not reviewed before the release or distribution of a lot or unit of final product.	79.0	NaN	1.0	NaN	NaN	NaN
	Donors were not tested for evidence of infection with relevant communicable disease agents.	75.0	NaN	NaN	NaN	NaN	NaN
	Failure to perform a thorough investigation of an unexplained discrepancy.	69.0	NaN	2.0	NaN	NaN	NaN
	HCT/Ps for which the donor eligibility determination was not performed were not prominently labeled with the appropriate warning statements.	68.0	NaN	NaN	NaN	NaN	NaN

	ProductType	Biologics	Devices	Drugs	Food/Cosmetics	Tobacco	Veterinary
	ShortDescription
ShortDescription	Procedures not in writing, fully followed	65.0	61.0	1587.0	197.0	NaN	222.0
	FD-1572, protocol compliance	204.0	9.0	1339.0	1.0	NaN	3.0
	Scientifically sound laboratory controls	32.0	43.0	994.0	114.0	NaN	148.0
	Investigations of discrepancies, failures	32.0	58.0	973.0	90.0	NaN	151.0
	Absence of Written Procedures	27.0	46.0	891.0	168.0	NaN	149.0
	Case history records- inadequate or inadequate	140.0	5.0	796.0	NaN	NaN	3.0
	Testing and release for distribution	4.0	16.0	669.0	73.0	NaN	117.0
	Written procedures not established/followed	6.0	19.0	640.0	97.0	NaN	103.0
	Cleaning / Sanitizing / Maintenance	4.0	26.0	632.0	73.0	NaN	101.0
	Control procedures to monitor and validate performance	13.0	37.0	612.0	90.0	NaN	85.0
	Calibration/Inspection/Checking not done	38.0	26.0	528.0	61.0	NaN	77.0
	Lack of written stability program	14.0	23.0	527.0	84.0	NaN	104.0
	Training--operations, GMPs, written procedures	15.0	17.0	522.0	91.0	NaN	74.0
	SOPs not followed / documented	65.0	29.0	486.0	35.0	NaN	86.0
	Prepared for each batch, include complete information	10.0	18.0	460.0	77.0	NaN	79.0

	FiscalYear
2011	37506
2012	33656
2010	31330
2013	27399
2014	26231
2015	24722
2017	24271
2009	23356
2016	22834
2018	20688
2019	19461
2020	10526
2021	7288
2022	3779

	Country/Area	Foreign	United States
	FiscalYear
FiscalYear	2009	1033	22323
	2010	1431	29899
	2011	3083	34423
	2012	4395	29261
	2013	4366	23033
	2014	4436	21795
	2015	5114	19608
	2016	4655	18179
	2017	3954	20317
	2018	3329	17359
	2019	3461	16000
	2020	1824	8702
	2021	12	7276
	2022	83	3696

	FiscalYear	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
	State
State	-	1033.0	1431.0	3083.0	4395.0	4366.0	4436.0	5114.0	4655.0	3954.0	3329.0	3461.0	1824.0	12.0	83.0
	Alabama	195.0	140.0	141.0	124.0	172.0	126.0	64.0	48.0	168.0	115.0	92.0	1.0	9.0	24.0
	Alaska	26.0	64.0	95.0	105.0	97.0	37.0	46.0	56.0	46.0	34.0	35.0	4.0	NaN	NaN
	American Samoa	NaN	32.0	5.0	NaN	2.0	NaN	4.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN
	Arizona	198.0	165.0	203.0	302.0	235.0	176.0	273.0	170.0	303.0	258.0	245.0	77.0	184.0	26.0
	Arkansas	51.0	109.0	84.0	199.0	140.0	103.0	88.0	63.0	132.0	51.0	81.0	13.0	13.0	2.0
	California	3048.0	4715.0	5182.0	3992.0	2894.0	2781.0	3322.0	2433.0	2330.0	2508.0	2311.0	1422.0	1616.0	765.0
	Colorado	605.0	911.0	947.0	734.0	568.0	504.0	483.0	378.0	507.0	381.0	334.0	260.0	104.0	55.0
	Connecticut	217.0	295.0	303.0	357.0	370.0	287.0	229.0	283.0	205.0	140.0	167.0	61.0	101.0	65.0
	Delaware	28.0	34.0	40.0	10.0	31.0	67.0	5.0	30.0	32.0	60.0	17.0	24.0	4.0	4.0
	District of Columbia	20.0	49.0	33.0	41.0	31.0	14.0	26.0	53.0	9.0	56.0	37.0	19.0	12.0	18.0
	Florida	2988.0	4308.0	4435.0	3719.0	3043.0	2698.0	1487.0	1727.0	2079.0	1775.0	1834.0	1029.0	564.0	208.0
	Georgia	423.0	787.0	771.0	527.0	459.0	435.0	316.0	430.0	504.0	351.0	286.0	195.0	105.0	29.0
	Guam	NaN	NaN	32.0	NaN	NaN	10.0	4.0	NaN	4.0	10.0	NaN	NaN	NaN	NaN
	Hawaii	39.0	135.0	147.0	219.0	96.0	114.0	125.0	94.0	83.0	74.0	87.0	33.0	22.0	8.0
	Idaho	97.0	172.0	226.0	144.0	184.0	117.0	96.0	60.0	129.0	51.0	77.0	45.0	50.0	6.0
	Illinois	1015.0	980.0	1510.0	1025.0	797.0	601.0	542.0	506.0	622.0	578.0	662.0	350.0	317.0	129.0
	Indiana	312.0	456.0	336.0	377.0	389.0	384.0	294.0	352.0	393.0	315.0	229.0	170.0	121.0	43.0
	Iowa	107.0	79.0	155.0	164.0	179.0	179.0	243.0	90.0	145.0	91.0	117.0	27.0	24.0	9.0
	Kansas	89.0	186.0	276.0	158.0	131.0	158.0	131.0	213.0	233.0	86.0	89.0	50.0	32.0	28.0
	Kentucky	238.0	400.0	548.0	401.0	287.0	336.0	277.0	326.0	431.0	262.0	139.0	42.0	93.0	41.0
	Louisiana	194.0	166.0	401.0	172.0	95.0	68.0	67.0	134.0	123.0	135.0	139.0	41.0	18.0	22.0
	Maine	179.0	143.0	187.0	482.0	199.0	292.0	199.0	129.0	138.0	131.0	141.0	32.0	32.0	19.0
	Maryland	333.0	465.0	357.0	424.0	371.0	379.0	206.0	352.0	369.0	208.0	186.0	221.0	65.0	18.0
	Massachusetts	702.0	890.0	1286.0	895.0	703.0	609.0	464.0	421.0	632.0	513.0	519.0	355.0	166.0	200.0
	Michigan	826.0	870.0	887.0	874.0	631.0	586.0	569.0	414.0	454.0	426.0	328.0	162.0	144.0	76.0
	Minnesota	433.0	576.0	631.0	541.0	433.0	358.0	328.0	282.0	453.0	360.0	320.0	148.0	167.0	89.0
	Mississippi	103.0	133.0	46.0	49.0	36.0	32.0	84.0	72.0	49.0	27.0	113.0	24.0	16.0	8.0
	Missouri	334.0	413.0	561.0	414.0	281.0	486.0	433.0	256.0	284.0	305.0	178.0	83.0	47.0	25.0
	Montana	135.0	71.0	64.0	82.0	70.0	92.0	27.0	31.0	19.0	31.0	20.0	14.0	1.0	2.0
	Nebraska	52.0	28.0	83.0	41.0	53.0	70.0	88.0	51.0	119.0	72.0	50.0	36.0	20.0	8.0
	Nevada	85.0	66.0	137.0	66.0	112.0	124.0	119.0	106.0	90.0	127.0	172.0	135.0	63.0	3.0
	New Hampshire	67.0	128.0	98.0	153.0	86.0	134.0	69.0	140.0	79.0	46.0	74.0	25.0	29.0	9.0
	New Jersey	860.0	931.0	1254.0	950.0	812.0	651.0	583.0	463.0	486.0	565.0	463.0	255.0	244.0	137.0
	New Mexico	165.0	140.0	249.0	79.0	101.0	175.0	66.0	107.0	143.0	96.0	30.0	38.0	49.0	11.0
	New York	1506.0	1895.0	2172.0	2262.0	1639.0	1871.0	1816.0	1462.0	1691.0	1510.0	1286.0	809.0	835.0	367.0
	North Carolina	997.0	899.0	503.0	502.0	342.0	311.0	310.0	352.0	393.0	393.0	453.0	251.0	123.0	80.0
	North Dakota	42.0	50.0	42.0	71.0	12.0	19.0	2.0	9.0	15.0	21.0	4.0	5.0	4.0	NaN
	Ohio	886.0	887.0	1270.0	826.0	620.0	881.0	903.0	921.0	1097.0	951.0	620.0	235.0	195.0	146.0

	ProductType	Biologics	Devices	Drugs	Food/Cosmetics	Tobacco	Veterinary
	LongDescription
LongDescription	There are no written procedures for production and process controls designed to assure that the drug products have the identity, strength, quality, and purity they purport or are represented to possess.	27.0	46.0	891.0	168.0	NaN	149.0
	The responsibilities and procedures applicable to the quality control unit are not fully followed.	43.0	32.0	656.0	59.0	NaN	92.0
	An investigation was not conducted in accordance with the signed statement of investigator and investigational plan.	88.0	3.0	645.0	1.0	NaN	NaN
	The responsibilities and procedures applicable to the quality control unit are not in writing and fully followed.	14.0	18.0	570.0	71.0	NaN	73.0
	An investigation was not conducted in accordance with the investigational plan.	104.0	101.0	569.0	NaN	NaN	2.0
	There is no written testing program designed to assess the stability characteristics of drug products.	14.0	23.0	527.0	84.0	NaN	104.0
	There is a failure to thoroughly review any unexplained discrepancy and the failure of a batch or any of its components to meet any of its specifications whether or not the batch has been already distributed.	11.0	32.0	453.0	47.0	NaN	71.0
	There is no quality control unit.	16.0	14.0	357.0	86.0	NaN	64.0
	Control procedures are not established which validate the performance of those manufacturing processes that may be responsible for causing variability in the characteristics of in-process material and the drug product.	11.0	19.0	340.0	64.0	NaN	55.0
	There is a failure to thoroughly review any unexplained discrepancy whether or not the batch has been already distributed.	14.0	12.0	334.0	29.0	NaN	46.0
	The responsibilities and procedures applicable to the quality control unit are not in writing.	7.0	11.0	329.0	65.0	NaN	52.0
	Batch production and control records do not include complete information relating to the production and control of each batch.	5.0	13.0	313.0	51.0	NaN	58.0
	Failure to prepare or maintain adequate and accurate case histories with respect to observations and data pertinent to the investigation.	52.0	2.0	311.0	NaN	NaN	1.0
	Written production and process control procedures are not followed in the execution of production and process control functions.	35.0	16.0	306.0	18.0	NaN	57.0
	Written procedures are not established for the cleaning and maintenance of equipment, including utensils, used in the manufacture, processing, packing or holding of a drug product.	5.0	9.0	296.0	52.0	NaN	45.0

	ProductType	Biologics	Devices	Drugs	Food/Cosmetics	Tobacco	Veterinary
	ShortDescription
ShortDescription	Record keeping	NaN	NaN	NaN	5.0	NaN	1700.0
	ELDU - producer causes Illegal drug residue	NaN	NaN	NaN	NaN	NaN	1185.0
	Drug inventory (control of drugs on premises)	NaN	NaN	NaN	4.0	NaN	563.0
	Expired drugs administered	NaN	NaN	NaN	2.0	NaN	338.0
	Individual identity of animal(s)	NaN	NaN	NaN	NaN	NaN	334.0
	ELDU without veterinary client-patient relationship	NaN	NaN	NaN	1.0	NaN	282.0
	Prescribed ELDU - vet causes an illegal drug residue	NaN	NaN	NaN	NaN	NaN	260.0
	Prescribed directions - not followed	NaN	NaN	NaN	NaN	NaN	250.0
	Procedures not in writing, fully followed	65.0	61.0	1587.0	197.0	NaN	222.0
	Records review prior to shipment for slaughter	NaN	NaN	NaN	NaN	NaN	219.0
	Drugs prohibited for extralabel use in food-producing animal(s)	NaN	NaN	NaN	2.0	NaN	219.0
	System for administration of drugs	NaN	NaN	NaN	1.0	NaN	173.0
	Dosage level	NaN	NaN	NaN	NaN	NaN	156.0
	Investigations of discrepancies, failures	32.0	58.0	973.0	90.0	NaN	151.0
	Absence of Written Procedures	27.0	46.0	891.0	168.0	NaN	149.0

	ProductType	Biologics	Devices	Drugs	Food/Cosmetics	Tobacco	Veterinary
	LongDescription
LongDescription	Causing a residue of an approved human or animal drug above an established safe level, safe concentration, or tolerance, through use of the drug contrary to its labeling.	NaN	NaN	NaN	NaN	NaN	1180.0
	Treatment records were not complete.	NaN	NaN	NaN	2.0	NaN	710.0
	Treatment records were not maintained.	NaN	NaN	NaN	2.0	NaN	562.0
	Treatment records were not maintained and complete.	NaN	NaN	NaN	1.0	NaN	359.0
	Expired drug(s) were observed in the drug storage area.	NaN	NaN	NaN	2.0	NaN	338.0
	Use of an animal drug in a manner contrary to label directions without benefit of a valid veterinary client-patient relationship.	NaN	NaN	NaN	1.0	NaN	264.0
	You lack an adequate inventory system for determining the quantities of drugs used to medicate your livestock.	NaN	NaN	NaN	NaN	NaN	228.0
	Failure to systematically review treatment records prior to offering an animal for slaughter for human food, to assure that drugs have been used only as directed and that appropriate withdrawal times have been observed.	NaN	NaN	NaN	NaN	NaN	217.0
	Failure to have a system to control administration of drug treatments to your animals.	NaN	NaN	NaN	1.0	NaN	171.0
	You lack an adequate inventory system for determining the quantities of drugs used to medicate your cows.	NaN	NaN	NaN	2.0	NaN	159.0
	Administration of an approved animal drug in excess of the indicated dosage, without benefit of a valid veterinarian-client-patient relationship.	NaN	NaN	NaN	NaN	NaN	156.0
	There are no written procedures for production and process controls designed to assure that the drug products have the identity, strength, quality, and purity they purport or are represented to possess.	27.0	46.0	891.0	168.0	NaN	149.0
	Administration of an approved animal drug contrary to the labeling, without benefit of a valid veterinarian-client-patient relationship, in that pre-slaughter withdrawal time was not observed.	NaN	NaN	NaN	1.0	NaN	139.0
	A prohibited drug was administered in an extralabel manner to a food-producing animal(s).	NaN	NaN	NaN	NaN	NaN	135.0
	You lack an adequate inventory system for determining the quantities of drugs used to medicate your cows and calves.	NaN	NaN	NaN	NaN	NaN	108.0

	ProductType	Biologics	Devices	Drugs	Food/Cosmetics	Tobacco	Veterinary
	ShortDescription
ShortDescription	Harborage areas	NaN	NaN	4.0	1960.0	1.0	39.0
	Storage requirements	NaN	NaN	1.0	1283.0	1.0	22.0
	Sufficient space	NaN	1.0	1.0	1139.0	1.0	9.0
	"Use ONLY with timer" label	NaN	3.0	NaN	NaN	NaN	NaN
	15 day reporting timeframe	5.0	2.0	NaN	NaN	NaN	NaN
	15 minute interval (S)	NaN	NaN	NaN	9.0	NaN	NaN
	24 hour retrieval of offsite records	NaN	NaN	NaN	5.0	NaN	NaN
	30-day sponsor notification to FDA	NaN	1.0	NaN	NaN	NaN	NaN
	50 mm classification warning	NaN	6.0	NaN	NaN	NaN	NaN
	60 day updates	NaN	NaN	NaN	2.0	NaN	NaN
	>50% of members not at RDRC meeting	NaN	NaN	2.0	NaN	NaN	NaN
	Abbreviated procedure	39.0	NaN	NaN	NaN	NaN	NaN
	Abbreviated requirements / non-significant risk study	1.0	10.0	NaN	NaN	NaN	NaN
	Absence of Written Procedures	27.0	46.0	891.0	168.0	NaN	149.0
	Accelerated stability studies	NaN	1.0	25.0	5.0	NaN	NaN

	ProductType	Biologics	Devices	Drugs	Food/Cosmetics	Tobacco	Veterinary
	LongDescription
LongDescription	Failure to properly store toxic cleaning compounds in a manner that protects against contamination of food.	NaN	NaN	1.0	98.0	1.0	3.0
	Failure to provide sufficient space for storage of materials as necessary for the maintenance of sanitary operations and the production of safe food.	NaN	1.0	NaN	732.0	1.0	7.0
	Failure to remove litter and waste and cut weeds or grass that may constitute an attractant, breeding place, or harborage area for pests, within the immediate vicinity of the plant buildings or structures.	NaN	NaN	1.0	202.0	1.0	2.0
	A Master Record File providing the complete procedure for manufacturing a specific product is not checked , dated and signed or initialed by a qualified person.	NaN	NaN	NaN	NaN	NaN	1.0
	A Master Record File providing the complete procedure for manufacturing a specific product is not dated and signed or initialed by a qualified person.	NaN	NaN	NaN	NaN	NaN	1.0
	A Master Record File providing the complete procedure for manufacturing a specific product is not signed or initialed by a qualified person.	NaN	NaN	NaN	NaN	NaN	2.0
	A justification for not reporting the correction or removal action to FDA that included by a designated person was not included in the record.	NaN	4.0	NaN	NaN	NaN	NaN
	A justification for not reporting the correction or removal action to FDA that included conclusions , follow-ups and reviews by a designated person was not included in the record.	NaN	1.0	NaN	NaN	NaN	NaN
	A justification for not reporting the correction or removal action to FDA that included conclusions and follow-ups and reviews by a designated person was not included in the record.	1.0	2.0	NaN	NaN	NaN	NaN
	A justification for not reporting the correction or removal action to FDA that included conclusions and reviews by a designated person was not included in the record.	NaN	9.0	NaN	NaN	NaN	NaN
	A justification for not reporting the correction or removal action to FDA that included conclusions by a designated person was not included in the record.	NaN	9.0	NaN	NaN	NaN	NaN
	A justification for not reporting the correction or removal action to FDA that included conclusions, follow-ups and reviews by a designated person was not included in the record.	NaN	44.0	NaN	NaN	NaN	NaN
	A responsible person did not document and date the determination that an HCT/P is available for distribution.	1.0	1.0	NaN	NaN	NaN	NaN
	A responsible person did not document the determination that an HCT/P is available for distribution.	2.0	2.0	NaN	NaN	NaN	NaN
	A 5 day report was not submitted to FDA on Form 3500A within 5 workdays of becoming aware that a reportable MDR event necessitates remedial action to prevent an unreasonable risk of substantial harm to the public health.	NaN	10.0	NaN	NaN	NaN	NaN

	Classification	No Action Indicated (NAI)	Official Action Indicated (OAI)	Voluntary Action Indicated (VAI)
	State
State	-	5656.0	6870.0	28650.0
	Alabama	127.0	403.0	889.0
	Alaska	104.0	11.0	530.0
	American Samoa	24.0	13.0	6.0
	Arizona	355.0	419.0	2041.0
	Arkansas	82.0	347.0	700.0
	California	5332.0	7086.0	26901.0
	Colorado	765.0	1343.0	4663.0
	Connecticut	302.0	369.0	2409.0
	Delaware	7.0	36.0	343.0
	District of Columbia	22.0	47.0	349.0
	Florida	8600.0	5402.0	17892.0
	Georgia	822.0	916.0	3880.0
	Guam	NaN	NaN	60.0
	Hawaii	75.0	395.0	806.0
	Idaho	87.0	391.0	976.0
	Illinois	1119.0	1560.0	6955.0
	Indiana	508.0	1029.0	2634.0
	Iowa	204.0	303.0	1102.0
	Kansas	232.0	288.0	1340.0
	Kentucky	800.0	439.0	2582.0
	Louisiana	101.0	385.0	1289.0
	Maine	122.0	426.0	1755.0
	Maryland	416.0	506.0	3032.0
	Massachusetts	703.0	784.0	6868.0
	Michigan	904.0	1736.0	4607.0
	Minnesota	524.0	663.0	3932.0
	Mississippi	35.0	340.0	417.0
	Missouri	439.0	667.0	2994.0
	Montana	31.0	128.0	500.0
	Nebraska	91.0	173.0	507.0
	Nevada	223.0	327.0	855.0
	New Hampshire	97.0	130.0	910.0
	New Jersey	725.0	1941.0	5988.0
	New Mexico	101.0	214.0	1134.0
	New York	1654.0	3234.0	16233.0
	North Carolina	1264.0	732.0	3913.0
	North Dakota	52.0	53.0	191.0
	Ohio	2784.0	1416.0	6238.0
	Oklahoma	234.0	339.0	994.0
	Oregon	339.0	520.0	2814.0
	Pennsylvania	1012.0	1270.0	6436.0
	Puerto Rico	681.0	1055.0	4034.0
	Rhode Island	100.0	204.0	1142.0
	South Carolina	304.0	337.0	1302.0
	South Dakota	13.0	69.0	145.0
	Tennessee	584.0	1467.0	2302.0
	Texas	1817.0	3203.0	10122.0
	Utah	628.0	1116.0	3150.0
	Vermont	65.0	231.0	817.0
	Virgin Islands	16.0	23.0	180.0
	Virginia	408.0	426.0	3146.0
	Washington	757.0	1163.0	6200.0
	West Virginia	48.0	138.0	499.0
	Wisconsin	565.0	1218.0	5074.0
	Wyoming	2.0	76.0	180.0